DSC/e Lecture Series

Speaker:
Marie-Jeanne Lesot

Title:
Extracting knowledge in linguistic form

Doors open at 12:00 h.

Abstract

Machine learning can be seen as aiming to allow users to understand the huge quantities of data they are faced with. One way to facilitate interpretation of the results consists in presenting them in natural language, offering linguistic expressions which may be easier to understand. The choice of such result formulation then has an impact on the machine learning techniques to be applied to the data. This talk will present three tasks in this framework, considering different types of data.

The first task aims at extracting gradual itemsets from numerical data, as well as contextual variants thereof, linguistically expressing information about the feature covariations, as illustrated by the example "the higher the speed, the greater the danger". A second task aims at summarising temporal series, in particular their periodicity, using the specific quantifier "regularly". In both cases, the question is to precisely define the associated semantics and to define efficient extraction algorithms. A third task investigates the measure of the relevance of the linguistic terms used to express the summaries, both with respect to the data structure, in case of linguistic variables, and with respect to the cognitive interpretation, in case of approximate numerical expressions.

Biography

Marie-Jeanne Lesot obtained her PhD in 2005 from the University Pierre et Marie Curie in Paris. Since 2006 she has been an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration and, in particular, to model and manage subjective information; they include similarity measures, fuzzy clustering, linguistic summaries, affective computing and information scoring.

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